Running an Automated Experiment

The Automated Experiment function in STEM allows you to sweep through a range of parameter values for a disease model, running and logging the results for every possible combination of parameter values. If you're trying to fit your model to a reference, for example, to actual incidence data collected by public health surveillance, manually walking the parameter space to find a good fit can be very time consuming and often impossible. That's where the Automated Experiment feature in STEM comes in handy.

First, to familiarize yourself with automated experiments in STEM, download the sample project available here:

Currently, there is no UI component in STEM that allows you to create an automated experiment. If you want to create one, you need to copy the AutomatedExperiment.automaticexperiment XML file from the sample project above into the experiments folder of your project. This is what the file looks like:

Next, you need to edit the file to fit your project environment. The following XML attributes and elements need to be updated:

uRI. Change this attribute so that it matches the name of your project. If you renamed the automatic experiment file, you also need to change the name here.

identifier (inside dublinCore). Same here, you need to change the value to match your project name and automatic experiment name.

baseScenario href. Change the value to match the name of the base scenario used for running the experiment

referenceDataDir. This is the directory containing the incidence reference data. The directory must contain the same files that STEM generates in its log output folders, e.g. an I_2.csv, Incidence_2.csv, S_2.csv etc. The only file used is the Incidence file, but the other files must be created (you can leave the values as 0). If the reference files are located in the project, we recommend using the environment neutral "platform:..." format to specify the location. This way the project can be shared with others.

You can configure various parameters for the Nelder Mead algorithm in this file. The parameters are:

tolerance: The error tolerance used to determine when the algorithm stops. If the calculated error has converged to a (local or global) minimum, the variation in the error is small. If the variation is smaller than the tolerance, the algorithm is done.

reInit: If reinit is true, the algorithm will re-start itself once its converged using the current best parameter values found. The parameter step sizes are reset to their original values. If the algorithm converges to the same minimum again (same error value), the algorithm, stops. If reinit is false, the algorithm stops after the first convergence. We recommend setting re-init to true to reduce the risk of getting stuck in a local minimum.

maximumNumberOfIterations: If this number of simulations has run without convergence to an error variation smaller than the tolerance, the algorithm stops or restarts itself (depending upon the setting of reInit)

Often convergence to a global minima can be reached quicker by manually restarting the automated experiment on the Controls tab. If it seems that the error variation is small, restart the algorithm again using the best set of parameters. This can often speed things up compared to waiting for the error variation to fall below the tolerance.